Alternatives to Apache Hive logo

Alternatives to Apache Hive

HBase, Apache Spark, Presto, Hadoop, and Apache Impala are the most popular alternatives and competitors to Apache Hive.
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What is Apache Hive and what are its top alternatives?

Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
Apache Hive is a tool in the Big Data Tools category of a tech stack.
Apache Hive is an open source tool with 3.8K GitHub stars and 3.6K GitHub forks. Here’s a link to Apache Hive's open source repository on GitHub

Top Alternatives to Apache Hive

  • HBase

    HBase

    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. ...

  • Apache Spark

    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

  • Presto

    Presto

    Distributed SQL Query Engine for Big Data

  • Hadoop

    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Apache Impala

    Apache Impala

    Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time. ...

  • Pig

    Pig

    Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce. ...

  • Snowflake

    Snowflake

    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn. ...

  • AWS Glue

    AWS Glue

    A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. ...

Apache Hive alternatives & related posts

HBase logo

HBase

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The Hadoop database, a distributed, scalable, big data store
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PROS OF HBASE
  • 8
    Performance
  • 5
    OLTP
  • 1
    Fast Point Queries
CONS OF HBASE
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    related HBase posts

    Hi, I'm building a machine learning pipelines to store image bytes and image vectors in the backend.

    So, when users query for the random access image data (key), we return the image bytes and perform machine learning model operations on it.

    I'm currently considering going with Amazon S3 (in the future, maybe add Redis caching layer) as the backend system to store the information (s3 buckets with sharded prefixes).

    As the latency of S3 is 100-200ms (get/put) and it has a high throughput of 3500 puts/sec and 5500 gets/sec for a given bucker/prefix. In the future I need to reduce the latency, I can add Redis cache.

    Also, s3 costs are way fewer than HBase (on Amazon EC2 instances with 3x replication factor)

    I have not personally used HBase before, so can someone help me if I'm making the right choice here? I'm not aware of Hbase latencies and I have learned that the MOB feature on Hbase has to be turned on if we have store image bytes on of the column families as the avg image bytes are 240Kb.

    See more
    Apache Spark logo

    Apache Spark

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    Fast and general engine for large-scale data processing
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    PROS OF APACHE SPARK
    • 58
      Open-source
    • 48
      Fast and Flexible
    • 7
      One platform for every big data problem
    • 6
      Easy to install and to use
    • 6
      Great for distributed SQL like applications
    • 3
      Works well for most Datascience usecases
    • 2
      Machine learning libratimery, Streaming in real
    • 2
      In memory Computation
    • 0
      Interactive Query
    CONS OF APACHE SPARK
    • 3
      Speed

    related Apache Spark posts

    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.8M views

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

    See more
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 935.6K views

    Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

    Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

    https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

    (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

    See more
    Presto logo

    Presto

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    Distributed SQL Query Engine for Big Data
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    PROS OF PRESTO
    • 15
      Works directly on files in s3 (no ETL)
    • 11
      Join multiple databases
    • 11
      Open-source
    • 8
      Scalable
    • 7
      Gets ready in minutes
    • 5
      MPP
    CONS OF PRESTO
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      related Presto posts

      Ashish Singh
      Tech Lead, Big Data Platform at Pinterest · | 35 upvotes · 746.2K views

      To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

      Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

      We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

      Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

      Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

      #BigData #AWS #DataScience #DataEngineering

      See more
      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.8M views

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

      See more
      Hadoop logo

      Hadoop

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      Open-source software for reliable, scalable, distributed computing
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      PROS OF HADOOP
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        Great ecosystem
      • 11
        One stack to rule them all
      • 4
        Great load balancer
      • 1
        Amazon aws
      • 1
        Java syntax
      CONS OF HADOOP
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        related Hadoop posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 935.6K views

        Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

        Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

        https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

        (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

        See more
        Shared insights
        on
        Kafka
        Hadoop
        at

        The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

        For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

        Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

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        Apache Impala logo

        Apache Impala

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        Real-time Query for Hadoop
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        PROS OF APACHE IMPALA
        • 10
          Super fast
        CONS OF APACHE IMPALA
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          related Apache Impala posts

          Pig logo

          Pig

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          Platform for analyzing large data sets
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          PROS OF PIG
          • 2
            Finer-grained control on parallelization
          • 1
            Proven at Petabyte scale
          • 1
            Open-source
          • 1
            Join optimizations for highly skewed data
          CONS OF PIG
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            related Pig posts

            Snowflake logo

            Snowflake

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            The data warehouse built for the cloud
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            PROS OF SNOWFLAKE
            • 2
              Good Performance
            • 1
              Public and Private Data Sharing
            • 1
              Multicloud
            • 1
              Great Documentation
            • 1
              Serverless
            CONS OF SNOWFLAKE
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              related Snowflake posts

              Shared insights
              on
              Google BigQuery
              Snowflake

              I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

              What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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              Shared insights
              on
              Snowflake
              Hadoop
              MarkLogic

              For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

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              AWS Glue logo

              AWS Glue

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              Fully managed extract, transform, and load (ETL) service
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              PROS OF AWS GLUE
              • 5
                Managed Hive Metastore
              CONS OF AWS GLUE
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                related AWS Glue posts

                Pardha Saradhi
                Technical Lead at Incred Financial Solutions · | 6 upvotes · 21.1K views

                Hi,

                We are currently storing the data in Amazon S3 using Apache Parquet format. We are using Presto to query the data from S3 and catalog it using AWS Glue catalog. We have Metabase sitting on top of Presto, where our reports are present. Currently, Presto is becoming too costly for us, and we are looking for alternatives for it but want to use the remaining setup (S3, Metabase) as much as possible. Please suggest alternative approaches.

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                Punith Ganadinni
                Senior Product Engineer · | 2 upvotes · 14.6K views

                Hey all, I need some suggestions in creating a replica of our RDS DB for reporting and analytical purposes. Cost is a major factor. I was thinking of using AWS Glue to move data from Amazon RDS to Amazon S3 and use Amazon Athena to run queries on it. Any other suggestions would be appreciable.

                See more